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          <h1 class="post-title" itemprop="name headline">Hive/Hive概论</h1>
        

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        <h1 id="1-什么是Hive"><a href="#1-什么是Hive" class="headerlink" title="1.什么是Hive"></a>1.什么是Hive</h1><ul>
<li><p>Hive : 由Facebook 开源用于解决 <strong><u>海量结构化日志</u></strong> 的数据统计。</p>
</li>
<li><p>Hive是基于Hadoop的一个<strong><u>数据仓库工具</u></strong>，可以将 <strong><u>结构化的数据文件映射为一张表</u></strong> ，并提供 <strong><u>类SQL</u></strong> 查询功能</p>
<p><strong><u>本质是：将HQL转为MapReduce程序</u></strong></p>
</li>
</ul>
<a id="more"></a>

<ul>
<li><p>Hive处理的数据存储在Hdfs</p>
</li>
<li><p>Hive分析数据底层的实现 <strong><u>默认是 MapReduce</u></strong> (可以换成其他的)</p>
</li>
<li><p>Hive运行程序是在yarn上的</p>
<p>总结：可以吧Hive理解为一个客户端，方便你去调度Hadoop，所以它不是一个分布式的框架，而是一个客户端工具。</p>
<p><img src="http://111.229.207.96:8080/hive/hive-01.png" alt=""></p>
</li>
</ul>
<h1 id="2-Hive的优缺点"><a href="#2-Hive的优缺点" class="headerlink" title="2.Hive的优缺点"></a>2.Hive的优缺点</h1><h2 id="2-1-优点"><a href="#2-1-优点" class="headerlink" title="2.1 优点"></a>2.1 优点</h2><ul>
<li>操作接口采用类SQL写法，提供快速开发能力</li>
<li>避免了写MapReduce，减少学习成本</li>
<li>Hive执行的延迟比较高，因此Hive常用于数据分析，对实时性要求不高的场景</li>
<li>Hive优势在于处理大数据，对于小数据处理没有优势</li>
<li>Hive支持用户自定义函数，用户可根据自己的需求定义函数</li>
</ul>
<h2 id="2-2缺点"><a href="#2-2缺点" class="headerlink" title="2.2缺点"></a>2.2缺点</h2><ul>
<li>Hive的HQL表达能力有限<ul>
<li>迭代式算法无法表达</li>
<li>数据挖掘方面不擅长</li>
</ul>
</li>
<li>Hive的效率比较低<ul>
<li>Hive自动生成的MapReduce作业，通常情况下不够智能</li>
<li>Hive调优比较困难，粒度较粗</li>
</ul>
</li>
</ul>
<h1 id="3-Hive架构"><a href="#3-Hive架构" class="headerlink" title="3.Hive架构"></a>3.Hive架构</h1><p><img src="http://111.229.207.96:8080/hive/hive02.jpg" alt=""></p>
<ul>
<li><p>解析器（SQL Parser）：将 SQL 字符串转换成抽象语法树 AST，这一步一般都用 第三方工具库完成，比如 antlr；对 AST 进行语法分析，比如表是否存在、字段是否存 在、SQL 语义是否有误。 </p>
</li>
<li><p>编译器（Physical Plan）：将 AST 编译生成逻辑执行计划。 </p>
</li>
<li><p>优化器（Query Optimizer）：对逻辑执行计划进行优化。 </p>
</li>
<li><p>执行器（Execution）：把逻辑执行计划转换成可以运行的物理计划。对于 Hive 来 说，就是 MR/Spark。</p>
</li>
</ul>
<h1 id="4-Hive运行机制"><a href="#4-Hive运行机制" class="headerlink" title="4 Hive运行机制"></a>4 Hive运行机制</h1><p><img src="http://111.229.207.96:8080/hive/hive03.jpg" alt=""></p>
<p>Hive 通过给用户提供的一系列交互接口，接收到用户的指令(SQL)，使用自己的 Driver， </p>
<p>结合元数据(MetaStore)，将这些指令翻译成 MapReduce，提交到 Hadoop 中执行，最后，将 </p>
<p>执行返回的结果输出到用户交互接口。</p>
<h1 id="5-Hive-和数据库比较"><a href="#5-Hive-和数据库比较" class="headerlink" title="5 Hive 和数据库比较"></a>5 <strong>Hive</strong> <strong>和数据库比较</strong></h1><p>由于 Hive 采用了类似 SQL 的查询语言 HQL(Hive Query Language)，因此很容易 将 Hive 理解为数据库。其实从结构上来看，<strong>Hive 和数据库除了拥有类似的查询语言，再无 类似之处</strong>。下面将从多个方面来阐述 Hive 和数据库的差异。数据库可以用在 Online 的应用 中，但是 Hive 是为数据仓库而设计的，清楚这一点，有助于从应用角度理解 Hive 的特性。</p>
<ul>
<li><strong>查询语言</strong> - 由于 SQL 被广泛的应用在数据仓库中，因此，专门针对 Hive 的特性设计了类 SQL 的查询语言 HQL。熟悉 SQL 开发的开发者可以很方便的使用 Hive 进行开发。</li>
<li><strong>数据存储位置</strong> - Hive 是建立在 Hadoop 之上的，所有 Hive 的数据都是存储在 HDFS 中的。而数据库则可以将数据保存在块设备或者本地文件系统中</li>
<li><strong>数据更新</strong> - 由于 Hive 是针对数据仓库应用设计的，而数据仓库的内容是<strong>读多写少</strong>的。因此，Hive 中<strong>不建议对数据的改写</strong>，所有的数据都是在加载的时候确定好的。而数据库中的数据通常是 需 要 经 常 进 行 修 改 的 ，因 此 可 以 使 用 INSERT INTO … VALUES 添 加 数 据 ， 使 用 UPDATE … SET 修改数据。</li>
<li><strong>索引</strong> - Hive 在加载数据的过程中不会对数据进行任何处理，甚至不会对数据进行扫描，因此 也没有对数据中的某些 Key 建立索引。Hive 要访问数据中满足条件的特定值时，需要暴力 扫描整个数据，因此访问延迟较高。由于 MapReduce 的引入， Hive 可以并行访问数据，因 此即使没有索引，对于大数据量的访问，Hive 仍然可以体现出优势。数据库中，通常会针 对一个或者几个列建立索引，因此对于少量的特定条件的数据的访问，数据库可以有很高的 效率，较低的延迟。由于数据的访问延迟较高，决定了 Hive 不适合在线数据查询。</li>
<li><strong>执行</strong> - Hive 中大多数查询的执行是通过 Hadoop 提供的 MapReduce 来实现的。而数据库通常 有自己的执行引擎。 </li>
<li><strong>执行延迟</strong> - Hive 在查询数据的时候，由于没有索引，需要扫描整个表，因此延迟较高。另外一个 导致 Hive 执行延迟高的因素是 MapReduce 框架。由于 MapReduce 本身具有较高的延迟， 因此在利用 MapReduce 执行 Hive 查询时，也会有较高的延迟。相对的，数据库的执行延迟 较低。当然，这个低是有条件的，即数据规模较小，当数据规模大到超过数据库的处理能力 的时候，Hive 的并行计算显然能体现出优势。</li>
<li><strong>可扩展性</strong> - 由于 Hive 是建立在 Hadoop 之上的，因此 Hive 的可扩展性是和 Hadoop 的可扩展性是一致（世界上最大的 Hadoop 集群在 Yahoo!，2009 年的规模在 4000 台节点左右）。而数 据库由于 ACID 语义的严格限制，扩展行非常有限。目前最先进的并行数据库 Oracle 在理 论上的扩展能力也只有 100 台左右。</li>
<li><strong>数据规模</strong> - 由于 Hive 建立在集群上并可以利用 MapReduce 进行并行计算，因此可以支持很大规模 的数据；对应的，数据库可以支持的数据规模较小。 </li>
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